% Abstract

\pdfbookmark[1]{Abstract (English)}{AbstractEN} % Bookmark name visible in a PDF viewer

\begingroup
%\let\clearpage\relax
\let\cleardoublepage\relax
\let\cleardoublepage\relax

\chapter*{Abstract} % Abstract name

This thesis proposes three distributed methods to achieve the allocation of an homogeneous swarm of robots to spatially distributed tasks.
Tasks are grouped together in space as to form clusters.

These methods have been developed in the form of probabilistic finite state machines, a microscopic level, behavior-based approach in Swarm Robotics.

The first method (Naive) simply consists of a greedy allocation of the robots to
available tasks in space, as soon as they have been localized.

The second one (Probabilistic) improves the Naive one with probabilistic rules to avoid allocation conflicts and achieve a more uniform allocation.

The third (Informed) is built upon the Probabilistic one, using odometry to avoid redundant exploration.

The methods have been simulated on three different scenarios: Uniform, Biased and Corridor.
We characterize the methods according to their allocation uniformity and allocation speed.

We show that there exists a trade-off between uniformity and speed for the developed methods.
We also find that the positioning of the clusters has a strong impact on the performances of the methods.
We conclude that the Informed method is the one having the best performances on the proposed scenarios.

\paragraph{Keywords:} Swarm Robotics, Task allocation, Decentralized control
\paragraph{Mots cl\'{e}s:} Robotique en essaim, Répartition des t\^{a}ches,  Contr\^{o}le décentralisé
 
\vfill
